Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations80577
Missing cells45560
Missing cells (%)2.2%
Duplicate rows17518
Duplicate rows (%)21.7%
Total size in memory30.3 MiB
Average record size in memory393.9 B

Variable types

Text2
Numeric13
Categorical10
DateTime1

Alerts

n_hog has constant value "1"Constant
Dataset has 17518 (21.7%) duplicate rowsDuplicates
p1 is highly overall correlated with p3High correlation
p2 is highly overall correlated with p4High correlation
p3 is highly overall correlated with p1High correlation
p4 is highly overall correlated with p2High correlation
p5 is highly overall correlated with p6High correlation
p6 is highly overall correlated with p5High correlation
h_mud is highly imbalanced (87.1%)Imbalance
p14 is highly imbalanced (61.3%)Imbalance
p15 is highly imbalanced (51.4%)Imbalance
ing has 45560 (56.5%) missing valuesMissing
con is highly skewed (γ1 = -25.15729878)Skewed

Reproduction

Analysis started2024-10-08 23:46:37.228044
Analysis finished2024-10-08 23:47:14.672018
Duration37.44 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

fol
Text

Distinct81
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2024-10-08T16:47:14.947652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters483462
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12A207
2nd row12A207
3rd row12A207
4th row12A207
5th row12A207
ValueCountFrequency (%)
11a201 1818
 
2.3%
11a208 1691
 
2.1%
11a205 1686
 
2.1%
11a218 1654
 
2.1%
11b211 1626
 
2.0%
11a216 1609
 
2.0%
11b201 1609
 
2.0%
11b198 1592
 
2.0%
11a206 1564
 
1.9%
12b195 1558
 
1.9%
Other values (71) 64170
79.6%
2024-10-08T16:47:15.438371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 167522
34.7%
2 102815
21.3%
A 40467
 
8.4%
0 40373
 
8.4%
B 40110
 
8.3%
3 22709
 
4.7%
9 19100
 
4.0%
4 10163
 
2.1%
8 10123
 
2.1%
6 10106
 
2.1%
Other values (2) 19974
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 483462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 167522
34.7%
2 102815
21.3%
A 40467
 
8.4%
0 40373
 
8.4%
B 40110
 
8.3%
3 22709
 
4.7%
9 19100
 
4.0%
4 10163
 
2.1%
8 10123
 
2.1%
6 10106
 
2.1%
Other values (2) 19974
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 483462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 167522
34.7%
2 102815
21.3%
A 40467
 
8.4%
0 40373
 
8.4%
B 40110
 
8.3%
3 22709
 
4.7%
9 19100
 
4.0%
4 10163
 
2.1%
8 10123
 
2.1%
6 10106
 
2.1%
Other values (2) 19974
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 483462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 167522
34.7%
2 102815
21.3%
A 40467
 
8.4%
0 40373
 
8.4%
B 40110
 
8.3%
3 22709
 
4.7%
9 19100
 
4.0%
4 10163
 
2.1%
8 10123
 
2.1%
6 10106
 
2.1%
Other values (2) 19974
 
4.1%

ent
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.686375
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:15.604682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median15
Q320
95-th percentile30
Maximum32
Range31
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.7992565
Coefficient of variation (CV)0.49719943
Kurtosis-0.6032358
Mean15.686375
Median Absolute Deviation (MAD)6
Skewness0.20110892
Sum1263961
Variance60.828403
MonotonicityNot monotonic
2024-10-08T16:47:15.752888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
19 9368
 
11.6%
14 8953
 
11.1%
9 7899
 
9.8%
15 6819
 
8.5%
11 3340
 
4.1%
21 2177
 
2.7%
1 2064
 
2.6%
5 2038
 
2.5%
7 1966
 
2.4%
30 1911
 
2.4%
Other values (22) 34042
42.2%
ValueCountFrequency (%)
1 2064
 
2.6%
2 1734
 
2.2%
3 1221
 
1.5%
4 1453
 
1.8%
5 2038
 
2.5%
6 1095
 
1.4%
7 1966
 
2.4%
8 1430
 
1.8%
9 7899
9.8%
10 1775
 
2.2%
ValueCountFrequency (%)
32 1507
1.9%
31 1510
1.9%
30 1911
2.4%
29 1563
1.9%
28 1537
1.9%
27 1477
1.8%
26 1449
1.8%
25 1841
2.3%
24 1556
1.9%
23 1606
2.0%

con
Real number (ℝ)

SKEWED 

Distinct657
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40285.871
Minimum22251
Maximum41418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:15.924726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22251
5-th percentile40031
Q140141
median40266
Q340382
95-th percentile40599
Maximum41418
Range19167
Interquartile range (IQR)241

Descriptive statistics

Standard deviation588.67161
Coefficient of variation (CV)0.014612359
Kurtosis773.25543
Mean40285.871
Median Absolute Deviation (MAD)120
Skewness-25.157299
Sum3.2461146 × 109
Variance346534.26
MonotonicityNot monotonic
2024-10-08T16:47:16.239102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40087 459
 
0.6%
40403 432
 
0.5%
40264 384
 
0.5%
40410 379
 
0.5%
40277 378
 
0.5%
40269 374
 
0.5%
40377 373
 
0.5%
40241 368
 
0.5%
40262 352
 
0.4%
40134 350
 
0.4%
Other values (647) 76728
95.2%
ValueCountFrequency (%)
22251 48
 
0.1%
22256 10
 
< 0.1%
22259 13
 
< 0.1%
40001 176
0.2%
40002 125
0.2%
40003 107
0.1%
40004 128
0.2%
40005 193
0.2%
40006 105
0.1%
40007 214
0.3%
ValueCountFrequency (%)
41418 44
0.1%
41417 48
0.1%
41415 32
 
< 0.1%
41408 12
 
< 0.1%
41405 36
 
< 0.1%
41398 72
0.1%
41396 17
 
< 0.1%
41388 12
 
< 0.1%
41387 42
0.1%
41380 104
0.1%

v_sel
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
20525 
1
20248 
2
19932 
4
19872 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

Length

2024-10-08T16:47:16.410938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:16.569091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

Most occurring characters

ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 20525
25.5%
1 20248
25.1%
2 19932
24.7%
4 19872
24.7%

n_hog
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
1
80577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 80577
100.0%

Length

2024-10-08T16:47:16.714678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:16.851416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 80577
100.0%

Most occurring characters

ValueCountFrequency (%)
1 80577
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 80577
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 80577
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 80577
100.0%

h_mud
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
0
77320 
1
 
3061
2
 
176
3
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

Length

2024-10-08T16:47:16.960763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:17.105194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 77320
96.0%
1 3061
 
3.8%
2 176
 
0.2%
3 20
 
< 0.1%

i_per
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
45118 
1
20313 
2
12447 
3
 
2180
5
 
442

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row1
5th row

Common Values

ValueCountFrequency (%)
45118
56.0%
1 20313
25.2%
2 12447
 
15.4%
3 2180
 
2.7%
5 442
 
0.5%
4 77
 
0.1%

Length

2024-10-08T16:47:17.245787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:17.399116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20313
57.3%
2 12447
35.1%
3 2180
 
6.1%
5 442
 
1.2%
4 77
 
0.2%

Most occurring characters

ValueCountFrequency (%)
45118
56.0%
1 20313
25.2%
2 12447
 
15.4%
3 2180
 
2.7%
5 442
 
0.5%
4 77
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
45118
56.0%
1 20313
25.2%
2 12447
 
15.4%
3 2180
 
2.7%
5 442
 
0.5%
4 77
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
45118
56.0%
1 20313
25.2%
2 12447
 
15.4%
3 2180
 
2.7%
5 442
 
0.5%
4 77
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
45118
56.0%
1 20313
25.2%
2 12447
 
15.4%
3 2180
 
2.7%
5 442
 
0.5%
4 77
 
0.1%

ing
Real number (ℝ)

MISSING 

Distinct236
Distinct (%)0.7%
Missing45560
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean399391.85
Minimum25
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:17.605136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile800
Q11900
median6500
Q3999999
95-th percentile999999
Maximum999999
Range999974
Interquartile range (IQR)998099

Descriptive statistics

Standard deviation487287
Coefficient of variation (CV)1.2200725
Kurtosis-1.822428
Mean399391.85
Median Absolute Deviation (MAD)5500
Skewness0.42108173
Sum1.3985504 × 1010
Variance2.3744862 × 1011
MonotonicityNot monotonic
2024-10-08T16:47:17.792591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 13899
 
17.2%
2000 1953
 
2.4%
1500 1764
 
2.2%
1000 1306
 
1.6%
1200 1211
 
1.5%
3000 1203
 
1.5%
2500 944
 
1.2%
5000 858
 
1.1%
4000 775
 
1.0%
9999 767
 
1.0%
Other values (226) 10337
 
12.8%
(Missing) 45560
56.5%
ValueCountFrequency (%)
25 1
 
< 0.1%
40 2
 
< 0.1%
50 8
 
< 0.1%
70 1
 
< 0.1%
80 2
 
< 0.1%
99 2
 
< 0.1%
100 39
< 0.1%
110 1
 
< 0.1%
120 7
 
< 0.1%
150 23
< 0.1%
ValueCountFrequency (%)
999999 13899
17.2%
250000 4
 
< 0.1%
100000 6
 
< 0.1%
80000 1
 
< 0.1%
75000 1
 
< 0.1%
70001 1
 
< 0.1%
70000 6
 
< 0.1%
60000 13
 
< 0.1%
55000 1
 
< 0.1%
50000 22
 
< 0.1%

mpio
Real number (ℝ)

Distinct80
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.892587
Minimum1
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:18.010472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median26
Q351
95-th percentile120
Maximum399
Range398
Interquartile range (IQR)45

Descriptive statistics

Standard deviation52.820117
Coefficient of variation (CV)1.2608464
Kurtosis14.961196
Mean41.892587
Median Absolute Deviation (MAD)21
Skewness3.1295518
Sum3375579
Variance2789.9647
MonotonicityNot monotonic
2024-10-08T16:47:18.213541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 5008
 
6.2%
5 4377
 
5.4%
6 3937
 
4.9%
101 3778
 
4.7%
1 3622
 
4.5%
4 3266
 
4.1%
20 3179
 
3.9%
30 3122
 
3.9%
2 2868
 
3.6%
17 2709
 
3.4%
Other values (70) 44711
55.5%
ValueCountFrequency (%)
1 3622
4.5%
2 2868
3.6%
3 2128
2.6%
4 3266
4.1%
5 4377
5.4%
6 3937
4.9%
7 2310
2.9%
8 751
 
0.9%
9 641
 
0.8%
10 1301
 
1.6%
ValueCountFrequency (%)
399 164
 
0.2%
390 186
 
0.2%
385 230
 
0.3%
350 176
 
0.2%
193 1430
1.8%
157 72
 
0.1%
140 37
 
< 0.1%
136 128
 
0.2%
133 136
 
0.2%
123 56
 
0.1%

ageb
Text

Distinct1778
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.1 MiB
2024-10-08T16:47:18.698461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9141566
Min length4

Characters and Unicode

Total characters395968
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row049-8
2nd row049-8
3rd row049-8
4th row049-8
5th row049-8
ValueCountFrequency (%)
036-3 236
 
0.3%
013-3 236
 
0.3%
021-a 230
 
0.3%
106-7 214
 
0.3%
022-5 207
 
0.3%
112-2 198
 
0.2%
024-6 195
 
0.2%
042-3 192
 
0.2%
095-7 190
 
0.2%
005-9 183
 
0.2%
Other values (1768) 78496
97.4%
2024-10-08T16:47:19.338088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 73660
18.6%
0 53474
13.5%
1 47113
11.9%
2 37451
9.5%
3 31864
8.0%
5 27969
 
7.1%
4 27067
 
6.8%
6 24489
 
6.2%
7 23001
 
5.8%
8 21410
 
5.4%
Other values (2) 28470
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 395968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 73660
18.6%
0 53474
13.5%
1 47113
11.9%
2 37451
9.5%
3 31864
8.0%
5 27969
 
7.1%
4 27067
 
6.8%
6 24489
 
6.2%
7 23001
 
5.8%
8 21410
 
5.4%
Other values (2) 28470
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 395968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 73660
18.6%
0 53474
13.5%
1 47113
11.9%
2 37451
9.5%
3 31864
8.0%
5 27969
 
7.1%
4 27067
 
6.8%
6 24489
 
6.2%
7 23001
 
5.8%
8 21410
 
5.4%
Other values (2) 28470
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 395968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 73660
18.6%
0 53474
13.5%
1 47113
11.9%
2 37451
9.5%
3 31864
8.0%
5 27969
 
7.1%
4 27067
 
6.8%
6 24489
 
6.2%
7 23001
 
5.8%
8 21410
 
5.4%
Other values (2) 28470
 
7.2%
Distinct242
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size629.6 KiB
Minimum2022-01-03 00:00:00
Maximum2022-12-21 00:00:00
2024-10-08T16:47:19.515125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:19.713724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

p1
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1476104
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:19.885582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81432764
Coefficient of variation (CV)0.25871297
Kurtosis-0.33032527
Mean3.1476104
Median Absolute Deviation (MAD)1
Skewness-0.07231958
Sum253625
Variance0.6631295
MonotonicityNot monotonic
2024-10-08T16:47:20.034993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 36481
45.3%
4 24683
30.6%
2 15724
19.5%
5 2572
 
3.2%
1 1112
 
1.4%
6 5
 
< 0.1%
ValueCountFrequency (%)
1 1112
 
1.4%
2 15724
19.5%
3 36481
45.3%
4 24683
30.6%
5 2572
 
3.2%
6 5
 
< 0.1%
ValueCountFrequency (%)
6 5
 
< 0.1%
5 2572
 
3.2%
4 24683
30.6%
3 36481
45.3%
2 15724
19.5%
1 1112
 
1.4%

p2
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8523152
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:20.165883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94838335
Coefficient of variation (CV)0.33249599
Kurtosis0.56427022
Mean2.8523152
Median Absolute Deviation (MAD)1
Skewness0.60973087
Sum229831
Variance0.89943098
MonotonicityNot monotonic
2024-10-08T16:47:20.290858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 31402
39.0%
2 28158
34.9%
4 14442
17.9%
1 3100
 
3.8%
5 2409
 
3.0%
6 1066
 
1.3%
ValueCountFrequency (%)
1 3100
 
3.8%
2 28158
34.9%
3 31402
39.0%
4 14442
17.9%
5 2409
 
3.0%
6 1066
 
1.3%
ValueCountFrequency (%)
6 1066
 
1.3%
5 2409
 
3.0%
4 14442
17.9%
3 31402
39.0%
2 28158
34.9%
1 3100
 
3.8%

p3
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0922099
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:20.454603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79021518
Coefficient of variation (CV)0.2555503
Kurtosis-0.18869267
Mean3.0922099
Median Absolute Deviation (MAD)1
Skewness-0.054623924
Sum249161
Variance0.62444003
MonotonicityNot monotonic
2024-10-08T16:47:20.588433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 39161
48.6%
4 22159
27.5%
2 16094
20.0%
5 1899
 
2.4%
1 1245
 
1.5%
6 19
 
< 0.1%
ValueCountFrequency (%)
1 1245
 
1.5%
2 16094
20.0%
3 39161
48.6%
4 22159
27.5%
5 1899
 
2.4%
6 19
 
< 0.1%
ValueCountFrequency (%)
6 19
 
< 0.1%
5 1899
 
2.4%
4 22159
27.5%
3 39161
48.6%
2 16094
20.0%
1 1245
 
1.5%

p4
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8029587
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:20.713406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92089696
Coefficient of variation (CV)0.32854461
Kurtosis0.71031686
Mean2.8029587
Median Absolute Deviation (MAD)1
Skewness0.64368702
Sum225854
Variance0.84805121
MonotonicityNot monotonic
2024-10-08T16:47:20.853998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 31690
39.3%
2 29625
36.8%
4 13294
16.5%
1 3104
 
3.9%
5 1930
 
2.4%
6 934
 
1.2%
ValueCountFrequency (%)
1 3104
 
3.9%
2 29625
36.8%
3 31690
39.3%
4 13294
16.5%
5 1930
 
2.4%
6 934
 
1.2%
ValueCountFrequency (%)
6 934
 
1.2%
5 1930
 
2.4%
4 13294
16.5%
3 31690
39.3%
2 29625
36.8%
1 3104
 
3.9%

p5
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4685084
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:20.991861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.85981476
Coefficient of variation (CV)0.24789179
Kurtosis-0.28768446
Mean3.4685084
Median Absolute Deviation (MAD)1
Skewness-0.37714723
Sum279482
Variance0.73928142
MonotonicityNot monotonic
2024-10-08T16:47:21.119514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 38179
47.4%
3 24182
30.0%
2 11466
 
14.2%
5 5982
 
7.4%
1 646
 
0.8%
6 122
 
0.2%
ValueCountFrequency (%)
1 646
 
0.8%
2 11466
 
14.2%
3 24182
30.0%
4 38179
47.4%
5 5982
 
7.4%
6 122
 
0.2%
ValueCountFrequency (%)
6 122
 
0.2%
5 5982
 
7.4%
4 38179
47.4%
3 24182
30.0%
2 11466
 
14.2%
1 646
 
0.8%

p6
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1728905
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:21.244485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0100054
Coefficient of variation (CV)0.31832344
Kurtosis-0.25699407
Mean3.1728905
Median Absolute Deviation (MAD)1
Skewness0.31142629
Sum255662
Variance1.020111
MonotonicityNot monotonic
2024-10-08T16:47:21.385052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28662
35.6%
4 22256
27.6%
2 20734
25.7%
5 6086
 
7.6%
1 1656
 
2.1%
6 1183
 
1.5%
ValueCountFrequency (%)
1 1656
 
2.1%
2 20734
25.7%
3 28662
35.6%
4 22256
27.6%
5 6086
 
7.6%
6 1183
 
1.5%
ValueCountFrequency (%)
6 1183
 
1.5%
5 6086
 
7.6%
4 22256
27.6%
3 28662
35.6%
2 20734
25.7%
1 1656
 
2.1%

p7
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
38706 
2
27516 
1
14329 
4
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

Length

2024-10-08T16:47:21.547467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:21.681797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 38706
48.0%
2 27516
34.1%
1 14329
 
17.8%
4 26
 
< 0.1%

p8
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
47683 
2
26689 
1
6132 
4
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

Length

2024-10-08T16:47:21.838013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:21.962983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 47683
59.2%
2 26689
33.1%
1 6132
 
7.6%
4 73
 
0.1%

p9
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2
56499 
1
23042 
3
 
1036

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

Length

2024-10-08T16:47:22.132981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:22.257952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

Most occurring characters

ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 56499
70.1%
1 23042
28.6%
3 1036
 
1.3%

p10
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2
53246 
1
24653 
4
 
2561
3
 
117

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

Length

2024-10-08T16:47:22.542113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:22.666162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 53246
66.1%
1 24653
30.6%
4 2561
 
3.2%
3 117
 
0.1%

p11
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0338062
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:22.806751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.87635286
Coefficient of variation (CV)0.28886251
Kurtosis0.46557188
Mean3.0338062
Median Absolute Deviation (MAD)1
Skewness0.46574516
Sum244455
Variance0.76799434
MonotonicityNot monotonic
2024-10-08T16:47:22.978560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 36873
45.8%
2 20852
25.9%
4 18157
22.5%
5 2656
 
3.3%
1 1202
 
1.5%
6 837
 
1.0%
ValueCountFrequency (%)
1 1202
 
1.5%
2 20852
25.9%
3 36873
45.8%
4 18157
22.5%
5 2656
 
3.3%
6 837
 
1.0%
ValueCountFrequency (%)
6 837
 
1.0%
5 2656
 
3.3%
4 18157
22.5%
3 36873
45.8%
2 20852
25.9%
1 1202
 
1.5%

p12
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.099619
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:23.207407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0899374
Coefficient of variation (CV)0.21372919
Kurtosis-0.25851009
Mean5.099619
Median Absolute Deviation (MAD)0
Skewness-0.8619346
Sum410912
Variance1.1879636
MonotonicityNot monotonic
2024-10-08T16:47:23.454388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 41324
51.3%
4 17515
21.7%
5 14013
 
17.4%
3 6309
 
7.8%
2 1042
 
1.3%
7 243
 
0.3%
1 131
 
0.2%
ValueCountFrequency (%)
1 131
 
0.2%
2 1042
 
1.3%
3 6309
 
7.8%
4 17515
21.7%
5 14013
 
17.4%
6 41324
51.3%
7 243
 
0.3%
ValueCountFrequency (%)
7 243
 
0.3%
6 41324
51.3%
5 14013
 
17.4%
4 17515
21.7%
3 6309
 
7.8%
2 1042
 
1.3%
1 131
 
0.2%

p13
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1357459
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.6 KiB
2024-10-08T16:47:23.604440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0520202
Coefficient of variation (CV)0.33549281
Kurtosis-0.039059401
Mean3.1357459
Median Absolute Deviation (MAD)1
Skewness0.43398808
Sum252669
Variance1.1067466
MonotonicityNot monotonic
2024-10-08T16:47:23.806118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 34924
43.3%
2 18675
23.2%
4 14205
17.6%
5 8566
 
10.6%
1 2869
 
3.6%
6 1338
 
1.7%
ValueCountFrequency (%)
1 2869
 
3.6%
2 18675
23.2%
3 34924
43.3%
4 14205
17.6%
5 8566
 
10.6%
6 1338
 
1.7%
ValueCountFrequency (%)
6 1338
 
1.7%
5 8566
 
10.6%
4 14205
17.6%
3 34924
43.3%
2 18675
23.2%
1 2869
 
3.6%

p14
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
68361 
2
 
6165
1
 
5951
4
 
100

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

Length

2024-10-08T16:47:24.017088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:24.417542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 68361
84.8%
2 6165
 
7.7%
1 5951
 
7.4%
4 100
 
0.1%

p15
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
3
63370 
1
9093 
2
8004 
4
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters80577
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Length

2024-10-08T16:47:24.636271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T16:47:24.819552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 63370
78.6%
1 9093
 
11.3%
2 8004
 
9.9%
4 110
 
0.1%

Interactions

2024-10-08T16:47:11.705313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:43.749012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:47.467634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:50.966615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.684248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.764876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.633394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.471086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.411762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.658775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.799567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.832834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.833412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:11.859277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:44.040863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:47.751053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:51.202243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.838897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.905056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.784233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.616475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.575129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.827702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.960211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.971379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.982357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.007215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:44.304343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:48.008368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:51.470381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.985947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.051633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.910618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.760092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.726704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.994196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.147338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.113725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.130666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.152940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:44.551826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:48.267512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:51.705926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.140781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.190505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.072320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.897179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.885896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:04.179090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.320969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.263481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.283985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.307192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:44.818565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:48.570643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:51.966142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.297557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.365748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.223147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.050922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.062847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:04.411820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.501057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.426806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.442880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.440052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:45.087943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:48.783790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:52.203467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.449249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.520393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.362822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.196420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.360477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:04.547071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.663226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.562843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.580211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.584303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:45.350073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:49.050406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:52.442831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.607182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.657153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.502522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.328976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.514316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:04.701654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.826818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.715087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.730891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.715433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:45.600545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:49.317076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:52.675192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.750931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.795708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.633768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.472888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.685292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:04.860715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:06.977495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.849186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.867584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.853893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:45.851537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:49.569679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:52.899126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:54.902076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:56.923805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.773386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.620440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.838634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.012898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.133062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:08.984766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:10.998163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:12.981467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:46.113052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:49.833635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.131858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.055172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.062298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:58.907831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.764406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:02.984689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.177704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.264222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.134247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:11.143773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:13.135178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:46.568117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:50.100022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.268734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.198144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.199913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.048960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:00.912113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.144741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.322320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.417252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.267623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:11.279471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:13.260634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:46.900836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:50.433267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.406434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.353173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.338506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.185567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.089405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.311306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.484423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.554360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.414882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:11.420046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:13.407566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:47.217637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:50.716424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:53.548962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:55.627470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:57.479782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:46:59.330666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:01.250752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:03.477934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:05.638216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:07.693115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:09.689232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T16:47:11.562597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-08T16:47:24.960171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
conenth_mudi_peringmpiop1p10p11p12p13p14p15p2p3p4p5p6p7p8p9v_sel
con1.0000.0560.0000.0000.0480.1420.0070.0180.0370.0260.0410.0060.0080.0040.0190.0020.018-0.0010.0160.0280.0120.012
ent0.0561.0000.0410.0580.0960.310-0.0710.093-0.095-0.041-0.0620.0980.104-0.032-0.082-0.040-0.094-0.0530.1300.1210.1360.028
h_mud0.0000.0411.0000.0110.0050.0590.0120.0110.0170.0070.0240.0070.0140.0180.0140.0190.0150.0280.0140.0110.0280.011
i_per0.0000.0580.0111.0000.1350.0220.0330.0640.0330.0230.0190.0380.0250.0290.0330.0260.0180.0170.0550.0500.0740.010
ing0.0480.0960.0050.1351.0000.0800.0010.025-0.0030.0240.0460.0440.0590.0280.0090.0220.0500.0410.0410.0390.0110.013
mpio0.1420.3100.0590.0220.0801.000-0.0170.035-0.0020.0290.0790.0340.0510.010-0.0240.0200.0090.0380.0410.0350.0450.021
p10.007-0.0710.0120.0330.001-0.0171.0000.2490.4510.2300.2810.1850.1790.4580.7600.4430.4680.3490.3730.3180.2890.014
p100.0180.0930.0110.0640.0250.0350.2491.0000.2490.1160.1480.1880.1980.1770.2330.1750.1600.1350.2870.2680.3750.014
p110.037-0.0950.0170.033-0.003-0.0020.4510.2491.0000.2760.3640.1960.2060.4790.4590.4720.3800.3880.2880.2850.3270.018
p120.026-0.0410.0070.0230.0240.0290.2300.1160.2761.0000.2770.0910.1060.2180.2320.2280.2920.2990.1440.1490.1650.021
p130.041-0.0620.0240.0190.0460.0790.2810.1480.3640.2771.0000.1170.1370.3140.2920.3150.3310.3860.1790.1750.2080.009
p140.0060.0980.0070.0380.0440.0340.1850.1880.1960.0910.1171.0000.3420.1660.1860.1630.1050.1020.2060.2190.2820.014
p150.0080.1040.0140.0250.0590.0510.1790.1980.2060.1060.1370.3421.0000.1680.1890.1710.1220.1150.2100.2230.2660.018
p20.004-0.0320.0180.0290.0280.0100.4580.1770.4790.2180.3140.1660.1681.0000.4670.7550.3640.4630.2570.2430.2630.020
p30.019-0.0820.0140.0330.009-0.0240.7600.2330.4590.2320.2920.1860.1890.4671.0000.4790.4610.3550.3530.3140.2910.018
p40.002-0.0400.0190.0260.0220.0200.4430.1750.4720.2280.3150.1630.1710.7550.4791.0000.3560.4650.2540.2390.2590.019
p50.018-0.0940.0150.0180.0500.0090.4680.1600.3800.2920.3310.1050.1220.3640.4610.3561.0000.5350.2530.2250.1960.013
p6-0.001-0.0530.0280.0170.0410.0380.3490.1350.3880.2990.3860.1020.1150.4630.3550.4650.5351.0000.2020.1890.2070.020
p70.0160.1300.0140.0550.0410.0410.3730.2870.2880.1440.1790.2060.2100.2570.3530.2540.2530.2021.0000.4040.3450.017
p80.0280.1210.0110.0500.0390.0350.3180.2680.2850.1490.1750.2190.2230.2430.3140.2390.2250.1890.4041.0000.3480.019
p90.0120.1360.0280.0740.0110.0450.2890.3750.3270.1650.2080.2820.2660.2630.2910.2590.1960.2070.3450.3481.0000.010
v_sel0.0120.0280.0110.0100.0130.0210.0140.0140.0180.0210.0090.0140.0180.0200.0180.0190.0130.0200.0170.0190.0101.000

Missing values

2024-10-08T16:47:13.641020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-08T16:47:14.172821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

folentconv_seln_hogh_mudi_peringmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15
012A20714000731011200.01049-82022-01-20333444332246433
112A20714000741035000.01049-82022-01-11323233332224432
212A20714000731011300.01049-82022-01-20333444332246433
312A20714000731011000.01049-82022-01-20333444332246433
412A207140007310NaN1049-82022-01-20333444332246433
512A207140007110NaN1049-82022-01-20333444232246433
612A20714000711025000.01049-82022-01-20333444232246433
712A20714000711025000.01049-82022-01-20333444232246433
812A20714000711026000.01049-82022-01-20333444232246433
912A207140007410315000.01049-82022-01-11323233332224432
folentconv_seln_hogh_mudi_peringmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15
8056712B2123240385310326000.056037-92022-12-19322222111122232
8056812B2123240385210NaN56037-92022-12-19343444332246533
8056912B2123240385210NaN56037-92022-12-19343444332246533
8057012B212324038511028700.056037-92022-12-19232333111123331
8057112B2123240385410NaN56037-92022-12-19444233332226333
8057212B2123240385410NaN56037-92022-12-19444233332226333
8057312B2123240385410NaN56037-92022-12-19444233332226333
8057412B212324038541024000.056037-92022-12-19444233332226333
8057512B2123240385310NaN56037-92022-12-19322222111122232
8057612B2123240385410NaN56037-92022-12-19444233332226333

Duplicate rows

Most frequently occurring

folentconv_seln_hogh_mudi_peringmpioagebfch_defp1p2p3p4p5p6p7p8p9p10p11p12p13p14p15# duplicates
58111A2021940364310NaN26095-72022-06-0422233411213643313
58211A2021940364310NaN26095-72022-07-0426244411212623313
58311A2021940364310NaN26095-72022-08-0422224412213633313
58411A2021940364310NaN26095-72022-09-0433333322123333313
243411A2081440156310NaN101145-02022-03-0734445533225633311
468911B1953140406210NaN50548-A2022-03-0932322233226613211
469011B1953140406210NaN50548-A2022-04-1121213233222423311
469211B1953140406210NaN50548A2022-02-1536323133326423311
243211A2081440156310NaN101145-02022-01-2033333223224433310
243511A2081440156310NaN101145-02022-12-0744444433224653310